{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from livelossplot import PlotLossesKeras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract the test.zip and train.zip files\n",
    "\n",
    "train_zip = zipfile.ZipFile('training.zip')\n",
    "train_zip.extractall()\n",
    "\n",
    "test_zip = zipfile.ZipFile('test.zip')\n",
    "test_zip.extractall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7049 entries, 0 to 7048\n",
      "Data columns (total 31 columns):\n",
      "left_eye_center_x            7039 non-null float64\n",
      "left_eye_center_y            7039 non-null float64\n",
      "right_eye_center_x           7036 non-null float64\n",
      "right_eye_center_y           7036 non-null float64\n",
      "left_eye_inner_corner_x      2271 non-null float64\n",
      "left_eye_inner_corner_y      2271 non-null float64\n",
      "left_eye_outer_corner_x      2267 non-null float64\n",
      "left_eye_outer_corner_y      2267 non-null float64\n",
      "right_eye_inner_corner_x     2268 non-null float64\n",
      "right_eye_inner_corner_y     2268 non-null float64\n",
      "right_eye_outer_corner_x     2268 non-null float64\n",
      "right_eye_outer_corner_y     2268 non-null float64\n",
      "left_eyebrow_inner_end_x     2270 non-null float64\n",
      "left_eyebrow_inner_end_y     2270 non-null float64\n",
      "left_eyebrow_outer_end_x     2225 non-null float64\n",
      "left_eyebrow_outer_end_y     2225 non-null float64\n",
      "right_eyebrow_inner_end_x    2270 non-null float64\n",
      "right_eyebrow_inner_end_y    2270 non-null float64\n",
      "right_eyebrow_outer_end_x    2236 non-null float64\n",
      "right_eyebrow_outer_end_y    2236 non-null float64\n",
      "nose_tip_x                   7049 non-null float64\n",
      "nose_tip_y                   7049 non-null float64\n",
      "mouth_left_corner_x          2269 non-null float64\n",
      "mouth_left_corner_y          2269 non-null float64\n",
      "mouth_right_corner_x         2270 non-null float64\n",
      "mouth_right_corner_y         2270 non-null float64\n",
      "mouth_center_top_lip_x       2275 non-null float64\n",
      "mouth_center_top_lip_y       2275 non-null float64\n",
      "mouth_center_bottom_lip_x    7016 non-null float64\n",
      "mouth_center_bottom_lip_y    7016 non-null float64\n",
      "Image                        7049 non-null object\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# read train.csv \n",
    "\n",
    "train_csv = pd.read_csv('training.csv')\n",
    "train_csv.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>left_eye_center_x</th>\n",
       "      <td>66.0336</td>\n",
       "      <td>64.3329</td>\n",
       "      <td>65.0571</td>\n",
       "      <td>65.2257</td>\n",
       "      <td>66.7253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left_eye_center_y</th>\n",
       "      <td>39.0023</td>\n",
       "      <td>34.9701</td>\n",
       "      <td>34.9096</td>\n",
       "      <td>37.2618</td>\n",
       "      <td>39.6213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_eye_center_x</th>\n",
       "      <td>30.227</td>\n",
       "      <td>29.9493</td>\n",
       "      <td>30.9038</td>\n",
       "      <td>32.0231</td>\n",
       "      <td>32.2448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_eye_center_y</th>\n",
       "      <td>36.4217</td>\n",
       "      <td>33.4487</td>\n",
       "      <td>34.9096</td>\n",
       "      <td>37.2618</td>\n",
       "      <td>38.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left_eye_inner_corner_x</th>\n",
       "      <td>59.5821</td>\n",
       "      <td>58.8562</td>\n",
       "      <td>59.412</td>\n",
       "      <td>60.0033</td>\n",
       "      <td>58.5659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left_eye_inner_corner_y</th>\n",
       "      <td>39.6474</td>\n",
       "      <td>35.2743</td>\n",
       "      <td>36.321</td>\n",
       "      <td>39.1272</td>\n",
       "      <td>39.6213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left_eye_outer_corner_x</th>\n",
       "      <td>73.1303</td>\n",
       "      <td>70.7227</td>\n",
       "      <td>70.9844</td>\n",
       "      <td>72.3147</td>\n",
       "      <td>72.5159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left_eye_outer_corner_y</th>\n",
       "      <td>39.97</td>\n",
       "      <td>36.1872</td>\n",
       "      <td>36.321</td>\n",
       "      <td>38.381</td>\n",
       "      <td>39.8845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_eye_inner_corner_x</th>\n",
       "      <td>36.3566</td>\n",
       "      <td>36.0347</td>\n",
       "      <td>37.6781</td>\n",
       "      <td>37.6186</td>\n",
       "      <td>36.9824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_eye_inner_corner_y</th>\n",
       "      <td>37.3894</td>\n",
       "      <td>34.3615</td>\n",
       "      <td>36.321</td>\n",
       "      <td>38.7541</td>\n",
       "      <td>39.0949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_eye_outer_corner_x</th>\n",
       "      <td>23.4529</td>\n",
       "      <td>24.4725</td>\n",
       "      <td>24.9764</td>\n",
       "      <td>25.3073</td>\n",
       "      <td>22.5061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_eye_outer_corner_y</th>\n",
       "      <td>37.3894</td>\n",
       "      <td>33.1444</td>\n",
       "      <td>36.6032</td>\n",
       "      <td>38.0079</td>\n",
       "      <td>38.3052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left_eyebrow_inner_end_x</th>\n",
       "      <td>56.9533</td>\n",
       "      <td>53.9874</td>\n",
       "      <td>55.7425</td>\n",
       "      <td>56.4338</td>\n",
       "      <td>57.2496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left_eyebrow_inner_end_y</th>\n",
       "      <td>29.0336</td>\n",
       "      <td>28.2759</td>\n",
       "      <td>27.5709</td>\n",
       "      <td>30.9299</td>\n",
       "      <td>30.6722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left_eyebrow_outer_end_x</th>\n",
       "      <td>80.2271</td>\n",
       "      <td>78.6342</td>\n",
       "      <td>78.8874</td>\n",
       "      <td>77.9103</td>\n",
       "      <td>77.7629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left_eyebrow_outer_end_y</th>\n",
       "      <td>32.2281</td>\n",
       "      <td>30.4059</td>\n",
       "      <td>32.6516</td>\n",
       "      <td>31.6657</td>\n",
       "      <td>31.7372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_eyebrow_inner_end_x</th>\n",
       "      <td>40.2276</td>\n",
       "      <td>42.7289</td>\n",
       "      <td>42.1939</td>\n",
       "      <td>41.6715</td>\n",
       "      <td>38.0354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_eyebrow_inner_end_y</th>\n",
       "      <td>29.0023</td>\n",
       "      <td>26.146</td>\n",
       "      <td>28.1355</td>\n",
       "      <td>31.05</td>\n",
       "      <td>30.9354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_eyebrow_outer_end_x</th>\n",
       "      <td>16.3564</td>\n",
       "      <td>16.8654</td>\n",
       "      <td>16.7912</td>\n",
       "      <td>20.458</td>\n",
       "      <td>15.9259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_eyebrow_outer_end_y</th>\n",
       "      <td>29.6475</td>\n",
       "      <td>27.0589</td>\n",
       "      <td>32.0871</td>\n",
       "      <td>29.9093</td>\n",
       "      <td>30.6722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nose_tip_x</th>\n",
       "      <td>44.4206</td>\n",
       "      <td>48.2063</td>\n",
       "      <td>47.5573</td>\n",
       "      <td>51.8851</td>\n",
       "      <td>43.2995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nose_tip_y</th>\n",
       "      <td>57.0668</td>\n",
       "      <td>55.6609</td>\n",
       "      <td>53.5389</td>\n",
       "      <td>54.1665</td>\n",
       "      <td>64.8895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mouth_left_corner_x</th>\n",
       "      <td>61.1953</td>\n",
       "      <td>56.4214</td>\n",
       "      <td>60.8229</td>\n",
       "      <td>65.5989</td>\n",
       "      <td>60.6714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mouth_left_corner_y</th>\n",
       "      <td>79.9702</td>\n",
       "      <td>76.352</td>\n",
       "      <td>73.0143</td>\n",
       "      <td>72.7037</td>\n",
       "      <td>77.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mouth_right_corner_x</th>\n",
       "      <td>28.6145</td>\n",
       "      <td>35.1224</td>\n",
       "      <td>33.7263</td>\n",
       "      <td>37.2455</td>\n",
       "      <td>31.1918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mouth_right_corner_y</th>\n",
       "      <td>77.389</td>\n",
       "      <td>76.0477</td>\n",
       "      <td>72.732</td>\n",
       "      <td>74.1955</td>\n",
       "      <td>76.9973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mouth_center_top_lip_x</th>\n",
       "      <td>43.3126</td>\n",
       "      <td>46.6846</td>\n",
       "      <td>47.2749</td>\n",
       "      <td>50.3032</td>\n",
       "      <td>44.9627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mouth_center_top_lip_y</th>\n",
       "      <td>72.9355</td>\n",
       "      <td>70.2666</td>\n",
       "      <td>70.1918</td>\n",
       "      <td>70.0917</td>\n",
       "      <td>73.7074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mouth_center_bottom_lip_x</th>\n",
       "      <td>43.1307</td>\n",
       "      <td>45.4679</td>\n",
       "      <td>47.2749</td>\n",
       "      <td>51.5612</td>\n",
       "      <td>44.2271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mouth_center_bottom_lip_y</th>\n",
       "      <td>84.4858</td>\n",
       "      <td>85.4802</td>\n",
       "      <td>78.6594</td>\n",
       "      <td>78.2684</td>\n",
       "      <td>86.8712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Image</th>\n",
       "      <td>238 236 237 238 240 240 239 241 241 243 240 23...</td>\n",
       "      <td>219 215 204 196 204 211 212 200 180 168 178 19...</td>\n",
       "      <td>144 142 159 180 188 188 184 180 167 132 84 59 ...</td>\n",
       "      <td>193 192 193 194 194 194 193 192 168 111 50 12 ...</td>\n",
       "      <td>147 148 160 196 215 214 216 217 219 220 206 18...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                           0  \\\n",
       "left_eye_center_x                                                    66.0336   \n",
       "left_eye_center_y                                                    39.0023   \n",
       "right_eye_center_x                                                    30.227   \n",
       "right_eye_center_y                                                   36.4217   \n",
       "left_eye_inner_corner_x                                              59.5821   \n",
       "left_eye_inner_corner_y                                              39.6474   \n",
       "left_eye_outer_corner_x                                              73.1303   \n",
       "left_eye_outer_corner_y                                                39.97   \n",
       "right_eye_inner_corner_x                                             36.3566   \n",
       "right_eye_inner_corner_y                                             37.3894   \n",
       "right_eye_outer_corner_x                                             23.4529   \n",
       "right_eye_outer_corner_y                                             37.3894   \n",
       "left_eyebrow_inner_end_x                                             56.9533   \n",
       "left_eyebrow_inner_end_y                                             29.0336   \n",
       "left_eyebrow_outer_end_x                                             80.2271   \n",
       "left_eyebrow_outer_end_y                                             32.2281   \n",
       "right_eyebrow_inner_end_x                                            40.2276   \n",
       "right_eyebrow_inner_end_y                                            29.0023   \n",
       "right_eyebrow_outer_end_x                                            16.3564   \n",
       "right_eyebrow_outer_end_y                                            29.6475   \n",
       "nose_tip_x                                                           44.4206   \n",
       "nose_tip_y                                                           57.0668   \n",
       "mouth_left_corner_x                                                  61.1953   \n",
       "mouth_left_corner_y                                                  79.9702   \n",
       "mouth_right_corner_x                                                 28.6145   \n",
       "mouth_right_corner_y                                                  77.389   \n",
       "mouth_center_top_lip_x                                               43.3126   \n",
       "mouth_center_top_lip_y                                               72.9355   \n",
       "mouth_center_bottom_lip_x                                            43.1307   \n",
       "mouth_center_bottom_lip_y                                            84.4858   \n",
       "Image                      238 236 237 238 240 240 239 241 241 243 240 23...   \n",
       "\n",
       "                                                                           1  \\\n",
       "left_eye_center_x                                                    64.3329   \n",
       "left_eye_center_y                                                    34.9701   \n",
       "right_eye_center_x                                                   29.9493   \n",
       "right_eye_center_y                                                   33.4487   \n",
       "left_eye_inner_corner_x                                              58.8562   \n",
       "left_eye_inner_corner_y                                              35.2743   \n",
       "left_eye_outer_corner_x                                              70.7227   \n",
       "left_eye_outer_corner_y                                              36.1872   \n",
       "right_eye_inner_corner_x                                             36.0347   \n",
       "right_eye_inner_corner_y                                             34.3615   \n",
       "right_eye_outer_corner_x                                             24.4725   \n",
       "right_eye_outer_corner_y                                             33.1444   \n",
       "left_eyebrow_inner_end_x                                             53.9874   \n",
       "left_eyebrow_inner_end_y                                             28.2759   \n",
       "left_eyebrow_outer_end_x                                             78.6342   \n",
       "left_eyebrow_outer_end_y                                             30.4059   \n",
       "right_eyebrow_inner_end_x                                            42.7289   \n",
       "right_eyebrow_inner_end_y                                             26.146   \n",
       "right_eyebrow_outer_end_x                                            16.8654   \n",
       "right_eyebrow_outer_end_y                                            27.0589   \n",
       "nose_tip_x                                                           48.2063   \n",
       "nose_tip_y                                                           55.6609   \n",
       "mouth_left_corner_x                                                  56.4214   \n",
       "mouth_left_corner_y                                                   76.352   \n",
       "mouth_right_corner_x                                                 35.1224   \n",
       "mouth_right_corner_y                                                 76.0477   \n",
       "mouth_center_top_lip_x                                               46.6846   \n",
       "mouth_center_top_lip_y                                               70.2666   \n",
       "mouth_center_bottom_lip_x                                            45.4679   \n",
       "mouth_center_bottom_lip_y                                            85.4802   \n",
       "Image                      219 215 204 196 204 211 212 200 180 168 178 19...   \n",
       "\n",
       "                                                                           2  \\\n",
       "left_eye_center_x                                                    65.0571   \n",
       "left_eye_center_y                                                    34.9096   \n",
       "right_eye_center_x                                                   30.9038   \n",
       "right_eye_center_y                                                   34.9096   \n",
       "left_eye_inner_corner_x                                               59.412   \n",
       "left_eye_inner_corner_y                                               36.321   \n",
       "left_eye_outer_corner_x                                              70.9844   \n",
       "left_eye_outer_corner_y                                               36.321   \n",
       "right_eye_inner_corner_x                                             37.6781   \n",
       "right_eye_inner_corner_y                                              36.321   \n",
       "right_eye_outer_corner_x                                             24.9764   \n",
       "right_eye_outer_corner_y                                             36.6032   \n",
       "left_eyebrow_inner_end_x                                             55.7425   \n",
       "left_eyebrow_inner_end_y                                             27.5709   \n",
       "left_eyebrow_outer_end_x                                             78.8874   \n",
       "left_eyebrow_outer_end_y                                             32.6516   \n",
       "right_eyebrow_inner_end_x                                            42.1939   \n",
       "right_eyebrow_inner_end_y                                            28.1355   \n",
       "right_eyebrow_outer_end_x                                            16.7912   \n",
       "right_eyebrow_outer_end_y                                            32.0871   \n",
       "nose_tip_x                                                           47.5573   \n",
       "nose_tip_y                                                           53.5389   \n",
       "mouth_left_corner_x                                                  60.8229   \n",
       "mouth_left_corner_y                                                  73.0143   \n",
       "mouth_right_corner_x                                                 33.7263   \n",
       "mouth_right_corner_y                                                  72.732   \n",
       "mouth_center_top_lip_x                                               47.2749   \n",
       "mouth_center_top_lip_y                                               70.1918   \n",
       "mouth_center_bottom_lip_x                                            47.2749   \n",
       "mouth_center_bottom_lip_y                                            78.6594   \n",
       "Image                      144 142 159 180 188 188 184 180 167 132 84 59 ...   \n",
       "\n",
       "                                                                           3  \\\n",
       "left_eye_center_x                                                    65.2257   \n",
       "left_eye_center_y                                                    37.2618   \n",
       "right_eye_center_x                                                   32.0231   \n",
       "right_eye_center_y                                                   37.2618   \n",
       "left_eye_inner_corner_x                                              60.0033   \n",
       "left_eye_inner_corner_y                                              39.1272   \n",
       "left_eye_outer_corner_x                                              72.3147   \n",
       "left_eye_outer_corner_y                                               38.381   \n",
       "right_eye_inner_corner_x                                             37.6186   \n",
       "right_eye_inner_corner_y                                             38.7541   \n",
       "right_eye_outer_corner_x                                             25.3073   \n",
       "right_eye_outer_corner_y                                             38.0079   \n",
       "left_eyebrow_inner_end_x                                             56.4338   \n",
       "left_eyebrow_inner_end_y                                             30.9299   \n",
       "left_eyebrow_outer_end_x                                             77.9103   \n",
       "left_eyebrow_outer_end_y                                             31.6657   \n",
       "right_eyebrow_inner_end_x                                            41.6715   \n",
       "right_eyebrow_inner_end_y                                              31.05   \n",
       "right_eyebrow_outer_end_x                                             20.458   \n",
       "right_eyebrow_outer_end_y                                            29.9093   \n",
       "nose_tip_x                                                           51.8851   \n",
       "nose_tip_y                                                           54.1665   \n",
       "mouth_left_corner_x                                                  65.5989   \n",
       "mouth_left_corner_y                                                  72.7037   \n",
       "mouth_right_corner_x                                                 37.2455   \n",
       "mouth_right_corner_y                                                 74.1955   \n",
       "mouth_center_top_lip_x                                               50.3032   \n",
       "mouth_center_top_lip_y                                               70.0917   \n",
       "mouth_center_bottom_lip_x                                            51.5612   \n",
       "mouth_center_bottom_lip_y                                            78.2684   \n",
       "Image                      193 192 193 194 194 194 193 192 168 111 50 12 ...   \n",
       "\n",
       "                                                                           4  \n",
       "left_eye_center_x                                                    66.7253  \n",
       "left_eye_center_y                                                    39.6213  \n",
       "right_eye_center_x                                                   32.2448  \n",
       "right_eye_center_y                                                    38.042  \n",
       "left_eye_inner_corner_x                                              58.5659  \n",
       "left_eye_inner_corner_y                                              39.6213  \n",
       "left_eye_outer_corner_x                                              72.5159  \n",
       "left_eye_outer_corner_y                                              39.8845  \n",
       "right_eye_inner_corner_x                                             36.9824  \n",
       "right_eye_inner_corner_y                                             39.0949  \n",
       "right_eye_outer_corner_x                                             22.5061  \n",
       "right_eye_outer_corner_y                                             38.3052  \n",
       "left_eyebrow_inner_end_x                                             57.2496  \n",
       "left_eyebrow_inner_end_y                                             30.6722  \n",
       "left_eyebrow_outer_end_x                                             77.7629  \n",
       "left_eyebrow_outer_end_y                                             31.7372  \n",
       "right_eyebrow_inner_end_x                                            38.0354  \n",
       "right_eyebrow_inner_end_y                                            30.9354  \n",
       "right_eyebrow_outer_end_x                                            15.9259  \n",
       "right_eyebrow_outer_end_y                                            30.6722  \n",
       "nose_tip_x                                                           43.2995  \n",
       "nose_tip_y                                                           64.8895  \n",
       "mouth_left_corner_x                                                  60.6714  \n",
       "mouth_left_corner_y                                                  77.5232  \n",
       "mouth_right_corner_x                                                 31.1918  \n",
       "mouth_right_corner_y                                                 76.9973  \n",
       "mouth_center_top_lip_x                                               44.9627  \n",
       "mouth_center_top_lip_y                                               73.7074  \n",
       "mouth_center_bottom_lip_x                                            44.2271  \n",
       "mouth_center_bottom_lip_y                                            86.8712  \n",
       "Image                      147 148 160 196 215 214 216 217 219 220 206 18...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_csv.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     28\n",
       "False     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "\n",
    "train_csv.isnull().any().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所以遗失的数据有 28 行。有两种解决一种是把所在的列也删掉，一种是用其他数据来填充遗失的数据。前者会减少数据，不建议这么做。所以用第二种方法处理遗失数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_csv.fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    31\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values after filling missing values\n",
    "\n",
    "train_csv.isnull().any().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺失的数据已经被填充了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来要做的就是还原图片数据，打印绘制图片。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7049,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = train_csv['Image']\n",
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "imag = []\n",
    "for i in range(0, 7049):\n",
    "    img = train_csv['Image'][i].split(' ') # 以空格来分隔\n",
    "    img = ['0' if x == '' else x for x in img]\n",
    "    imag.append(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7049, 9216)\n"
     ]
    }
   ],
   "source": [
    "# reshape and convert it into float value \n",
    "\n",
    "image_list = np.array(imag, dtype='float')\n",
    "print(image_list.shape)\n",
    "X_train = image_list.reshape(-1, 96, 96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the image\n",
    "\n",
    "plt.imshow(X_train[20], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate labels\n",
    "\n",
    "# Drop specified labels from rows or columns,1 means columns\n",
    "training = train_csv.drop('Image', axis=1) \n",
    "\n",
    "y_train = []\n",
    "for i in range(0, 7049):\n",
    "    y = training.iloc[i, :]\n",
    "    y_train.append(y)\n",
    "    \n",
    "y_train = np.array(y_train, dtype='float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Conv2D, Dropout, Dense, Flatten\n",
    "from keras.models import Sequential\n",
    "from keras import optimizers\n",
    "\n",
    "model = Sequential([Flatten(input_shape=(96,96)),\n",
    "                     Dense(128, activation=\"relu\"),\n",
    "                     Dropout(0.1),\n",
    "                     Dense(64, activation=\"relu\"),\n",
    "                     Dense(30)\n",
    "                       ])\n",
    "adam = optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
    "model.compile(optimizer=adam,\n",
    "              loss='mse',\n",
    "              metrics=['mae', 'accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse (cost function):\n",
      "training   (min:   50.933, max: 2479.020, cur:   53.660)\n",
      "validation (min:   87.946, max:  793.761, cur:   93.733)\n",
      "\n",
      "Mean absolute error:\n",
      "training   (min:    5.185, max:   36.296, cur:    5.225)\n",
      "validation (min:    6.769, max:   23.425, cur:    7.008)\n",
      "\n",
      "Accuracy:\n",
      "training   (min:    0.024, max:    0.634, cur:    0.628)\n",
      "validation (min:    0.052, max:    0.687, cur:    0.664)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x26def1d92e8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, \n",
    "          epochs=500, batch_size=128, \n",
    "          validation_split=0.2,\n",
    "          callbacks=[PlotLossesKeras()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1783 entries, 0 to 1782\n",
      "Data columns (total 2 columns):\n",
      "ImageId    1783 non-null int64\n",
      "Image      1783 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 27.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# read test.csv \n",
    "\n",
    "test_csv = pd.read_csv('test.csv')\n",
    "test_csv.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparing test data\n",
    "\n",
    "timag = []\n",
    "for i in range(0, 1783):\n",
    "    timg = test_csv['Image'][i].split(' ')\n",
    "    timg = ['0' if x == '' else x for x in timg]\n",
    "    \n",
    "    timag.append(timg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "timage_list = np.array(timag, dtype='float')\n",
    "X_test = timage_list.reshape(-1, 96, 96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_test[0], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1783, 30)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookid_csv = pd.read_csv(\"IdLookupTable.csv\")\n",
    "lookid_list = list(lookid_csv['FeatureName'])\n",
    "imageID = list(lookid_csv['ImageId'] - 1)\n",
    "pre_list = list(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rowid = lookid_csv['RowId']\n",
    "rowid = list(rowid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = []\n",
    "for f in list(lookid_csv['FeatureName']):\n",
    "    feature.append(lookid_list.index(f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "preded = []\n",
    "for x,y in zip(imageID, feature):\n",
    "    preded.append(pre_list[x][y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "rowid = pd.Series(rowid, name='RowId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc = pd.Series(preded, name='Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.concat([rowid,loc], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('face_key_detection_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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